Prediction of soil properties based on characteristic wavelengths with optimal spectral resolution by using Vis-NIR spectroscopy

Visible and near-infrared (Vis-NIR) spectroscopy technique has been recognized as a cost-effective, rapid, non-destructive alternative to traditional soil physicochemical analysis to estimate soil properties over the past few decades. Most efforts are devoted to the selection of characteristic wavelengths to eliminate the uninfor-mative variables while ignoring the impact of the spectral resolution of these wavelengths on the prediction accuracy of soil properties. Therefore, the originality of this study is to identify the characteristic wavelengths with the optimal spectral resolution to achieve a better prediction performance. A 'two-step' wavelength se-lection method was proposed to select the characteristic wavelengths. Then, we simulated 1 nm-100 nm spectral resolution based on the spectral database measured by a portable ASD spectroradiometer and adopted the artificial bee colony (ABC) algorithm to further improve the prediction ability by configuring the most appro-priate spectral resolution for each characteristic wavelength. The soil databases for this study consisted of 112 soil samples collected from Songnen Plain area in northeast China, and partial least squares regression (PLSR) was used to establish relations between pretreatment spectra and soil properties, including soil organic matter (SOM), available phosphorus (AP), and available potassium (AK). The independent validation results of this strategy effectively favored the prediction accuracy of SOM (RMSEp = 2.758, R2p = 0.857, RPIQp = 4.579), AP (RMSEp = 0.062, R2p = 0.790, RPIQp = 3.552), and AK (RMSEp = 0.366, R2p = 0.758, RPIQp = 2.525) compared with the PLSR models developed with full-spectra. In general, the method presented in this study suggested a framework for selecting characteristic wavelengths with optimal spectral solutions to predict SOM, AP, AK, and perhaps some other soil properties. The results of this paper also will provide guidance for the development of the low-cost specialized spectroscopic instruments for soil properties measurement.

Automated summary

Visible and near-infrared (Vis-NIR) spectroscopy technique has been recognized as a cost-effective, rapid, non-destructive alternative to traditional soil physicochemical analysis to estimate soil properties. This study proposes a method to select characteristic wavelengths with optimal spectral resolution to improve the prediction performance. By using a 'two-step' wavelength selection method and the artificial bee colony (ABC) algorithm, a better prediction accuracy for soil properties was obtained compared to using full-spectra models.